Poor trunk posture, especially during long periods of sitting, could lead to many issues such as Low Back Pain (LBP) and Forward Head Posture (FHP). Typical solutions are based on visual or vibration-based feedback. However, these feedback systems could lead to feedback being ignored by user and phantom vibration syndrome, respectively. Moreover, trunk posture needs to be corrected during rehabilitation of stroke patients to reduce trunk compensation. Proposed solutions in the literature to reduce trunk compensation primarily include strapping patients to the chair, which has several disadvantages. In this study, we propose using augmented haptic feedback for postural adaptation. In this two-part study, twenty-four healthy participants (age 25.87 ± 2.17 years) adapted to three different postural targets in the anterior direction while performing a unimanual reaching task using a robotic device. Results suggest a strong adaptation to the desired postural targets. Mean anterior trunk bending after intervention is significantly different as compared to baseline measurements for all postural targets. Additional analysis of movement straightness and smoothness indicates an absence of any negative interference of posture-based feedback on the performance of reaching movement. Taken together, these results suggest that augmented haptic feedback-based system could be used for postural adaptation applications.

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Motor learning is an essential component of human behavior. Many different factors can influence the process of motor learning, such as the amount of practice and type of feedback. Changes in task difficulty during training can also considerably impact motor learning. Typical motor learning studies include a sequential variation of task difficulty, i.e., easy to challenging, irrespective of user performance. However, many studies have reported the importance of performance-based task difficulty variation for effective motor learning and skill transfer. A performance-based adaptive algorithm for task difficulty variation based on the challenge-point framework is proposed in this study. The algorithm is described for postural adaptation during simultaneous upper-limb training. Ten healthy participants (28 ± 2.44 years) were recruited to validate the algorithm. Participants adapted to a postural target of 20° in the anterior direction from the initial upright posture while performing a unimanual reaching task using a robotic device. Results suggest a significant decrease in postural error after training. The algorithm successfully adapted the task difficulty based on the performance of the user. The proposed algorithm could be modified for different motor skills and can be further evaluated for different applications in order to maximize the potential benefits of rehabilitation sessions.